Personal warning temperature (PWT)

ABSTRACT

The Personal Warning Temperature (PWT) is a method of determining a person&#39;s personal warning temperature that defines a fever for that person. The method includes analyzing a person&#39;s body temperature measurements taken over time when the person is healthy, in order to find the person&#39;s average temperature; standard deviation; a channel defined by an upper and lower temperature bound using the average and standard deviation, and a selected multiplier for the standard deviation, such that nearly all body temperatures fall within the channel; and a personal warning temperature similarly defined using the average, standard deviation, and another selected multiplier for the standard deviation. The method also determines a correction or interpretation of a person&#39;s body temperature based on the time of day to account for known variations associated with circadian rhythm. A person can use their personal warning temperature for improved and accelerated awareness of their health status.

CROSS-REFERENCE TO RELATED APPLICATIONS

This divisional patent application claims priority of U.S. provisionalpatent application Ser. No. 16/986,359, filed on Aug. 6, 2020, titled“Personal Warning Temperature (PWT).” The priority application isincorporated herein, in its entirety.

FEDERALLY SPONSORED RESEARCH

NONE

BACKGROUND OF THE INVENTION Field of Invention

The invention relates to the field of medical screening and healthmonitoring; and particularly to reduce the spread of COVID-19 and otherinfectious diseases. The invention reduces or prevents the spread ofinfectious diseases by enabling the early detection of body temperatureabnormalities based on personal warning temperatures that are otherwisenot noticed by widely accepted minimum temperature thresholds foridentification of fever.

Description of the Related Art

Fever is defined as having a body temperature above the normal range.When a person is healthy, body temperature naturally varies for avariety of factors such as sex, time of day, current metabolic activity,ambient temperature, and biological events. When a person is nothealthy, body temperature may become elevated as fever is a common,natural response to infection. It is helpful to know the expected bodytemperature of an individual so that a person can determine whether ornot they are healthy. There have been many medical studies conducted todetermine the average body temperature and the average fevertemperature, or the range of these temperatures. For purposes ofsimplicity, the US Centers for Disease Control (CDC) advise that aperson who has a temperature of 100.4 degrees Fahrenheit or above has afever. That value of 100.4 degrees Fahrenheit comes from researchpublished in 1868 by the German doctor Carl Reinhold August Wunderlich.Today, defining a fever as 100.4 degrees Fahrenheit is considered a bitincorrect. However, for many infectious diseases, there is a largedifference between normal and fever temperatures, so this definition isoften useful anyway.

For COVID-19, people often have very mild symptoms, or symptoms may bedelayed. Even though fever is a significant symptom for COVID-19, peoplewith elevated temperatures are often considered asymptomatic if theirfever fails to rise to 100.4 degrees Fahrenheit. However, for COVID-19,defining a fever as 100.4 degrees Fahrenheit is hindering management ofthe pandemic. The reason is that people believe that they are not sickwhen they are in fact sick. Because people with COVID-19 believe theyare not sick, they interact with other persons and unintentionallyinfect them as well. COVID-19 is not the only infectious disease thatsometimes displays mild symptoms. Furthermore, unlike many otherdiseases, COVID-19 symptoms may be delayed. As a result, early warningof covid-19 identification is very important; yet further hindered bydefining a fever as 100.4 degrees Fahrenheit or higher.

The current state of the art is insufficient to control the spread ofthe coronavirus and other infectious diseases. It is based on old,outdated data and people are grasping at non-medical, non-FDA approved,very expensive, and flashy technologies that do not reach the resolutionfor finding and identifying mild symptoms.

SUMMARY OF THE INVENTION

The Personal Warning Temperature (PWT) method of the present inventionovercomes the deficiencies of prior-art methods for defining a fever, bycreating an individualized personal warning temperature for every personusing a set of their own body temperature measurements taken when theyare healthy. It is commonly known that normal body temperature varies byindividual for a variety of reasons; perhaps including body weight, ageand metabolism. As a result, fever, which is a body temperature higherthan normal, also varies between individuals. For particularindividuals, PWT uses statistical measures such as average (or mean) andstandard deviation to define a personal warning temperature, whichdefines a fever for that specific person. PWT assumes normal bodytemperature taken over time is normally distributed in the statisticalsense, and it uses some multiple of standard deviation above theperson's average normal temperature in order to define the personalwarning temperature for the specific individual.

Although it is not necessary to display body temperatures in atime-based graph in any specific exemplary embodiment, doing sofacilitates understanding. Therefore, body temperature is often part ofan exemplary embodiment. Similarly, body temperature is not necessary inorder to calculate or display a personal channel of expected normal bodytemperatures in order to calculate or know a person's personal warningtemperature. Displaying the channel also facilitates understanding andis often displayed on a graph, along with the personal warningtemperature of the individual.

Not only is a personal warning temperature more accurate at identifyingfever, it also triggers earlier because it is generally lower than thetraditional definition of 100.4 degrees Fahrenheit. By accuratelydefining a fever and identifying a fever earlier, people with thepotential to infect others with disease can take appropriate action atan earlier date, in order to avoid or reduce the chances of infectingothers.

Knowing your personal warning temperature, and specifically knowing whenyour body temperature measurement rises above your personal warningtemperature, creates a new capability to provide early warning ofpossible illness, infectiousness, and ability to appropriately react ina timely manner, in order to help manage an epidemic, pandemic, or otherhealth crisis with visibility that has never been known before. Yourpersonal warning temperature is more accurate and usually lower than thetraditional definition of a fever such as 100.4 degrees Fahrenheit. Thisresults in the feasibility of early warning when contracting a disease.

When displaying a person's body temperature data on a graph, it easy tosee that, when healthy, the person's body temperature is usually withina channel. It is also relatively easy to determine when the person'sbody temperature rises above their personal warning temperature. Thegraph, with the personal warning temperature displayed, removes the needto remember their personal warning temperature.

In the case of COVID-19 and perhaps other infectious diseases, peoplewith mild symptoms of the disease are not currently being detected whenthey are infectious; and thus they do not take precautions to avoidinfecting others. Temperature screening devices are not identifyingthese people either because the devices are often set to trigger at100.4 degrees, which is the traditional definition of fever. PWT canmore accurately and more quickly inform people when they are gettingsick. If every person knew their personal warning temperature andmonitored their body temperature daily, we would be in a much betterposition to manage the COVID-19 pandemic, and perhaps future pandemics.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 provides a graphical representation of a temperature chart withthe traditional fever temperature bar of 100.4 Fahrenheit.

FIG. 2 provides a graphical representation of the results of anindividual taking their personal temperature over time.

FIG. 3 shows a graphical representation of computing a personaltemperature average, standard deviation channel, and personal warningtemperature.

FIG. 4 shows a graphical representation of personal temperatures takenwith the recorded time of day.

FIG. 5 shows a graphical representation of using personal temperaturescollected and grouped in the morning; then computing averages, standarddeviation, and a new lower personal warning temperature.

FIG. 6 shows a graphical representation of using personal temperaturescollected and grouped in the evening; then computing averages, standarddeviation, and a new higher personal warning temperature.

FIG. 7 shows a graphical representation of grouping an individual'spersonal temperatures by time of day modeling the normal temperaturerise and fall thought the day; and calculating the averages by time suchas each hour and the new personal warning temperature based on time ofday.

LIST OF REFERENCE NUMERALS FOUND IN THE DRAWINGS

Element 1 represents a graphical representation of temperatures taken byan individual; warning lines, and timelines.

Element 2 represents the standard used fever temperature of 100.4 line.

Element 3 is a timeline in days.

Element 4 is a temperature scale.

Element 20 are personal temperatures taken by an individual.

Element 31 represents the lower bound of the personal temperaturestandard deviation channel (SDC).

Element 32 represents the average personal temperature line (APT).

Element 33 represents the upper bound of the personal temperaturestandard deviation channel (SDC).

Element 34 represents the personal warning temperature (PWT) line.

Element 40 represents personal temperatures taken in the morning.

Element 41 represents personal temperatures taken in the evening.

Element 52 represents the new average personal temperature line (APT)based only on morning temperatures.

Element 54 represents the new personal warning temperature (PWT) linebased on only morning temperatures.

Element 55 represents the change in the personal warning temperatureline based on using only morning temperatures.

Element 56 represents the change in average personal temperature (APT)based only on evening temperatures.

Element 62 represents the new average personal temperature line (APT)base only on morning temperatures.

Element 64 represents the new personal warning temperature (PWT) linebased on only evening temperatures.

Element 65 represents the change in the personal warning temperatureline based using only evening temperatures.

Element 66 represents the change in average personal temperature (APT)based only on evening temperatures.

Element 70 represents time of day that the personal temperatures weretaken.

Element 71 represents the average temperature based on time of day thattemperature was taken.

Element 72 represents the personal warning temperature (PWT) line basedon time of day that temperature was taken.

DETAILED DESCRIPTION OF THE EXEMPLARY EMBODIMENTS

In an exemplary embodiment of the Personal Warning Temperature (PWT) ofthe present invention, a healthy person's body temperature measurementsare used to calculate an average (or mean) temperature; as well as astandard deviation. A multiplier (M1) of the standard deviation, is usedto define a channel of expected normal temperatures, which are definedby lower and upper temperature bounds which are above and below theaverage temperature by the same magnitude. The multiplier is selected sothat nearly all the healthy person's body temperatures are within thechannel. It is well known that, for normally distributed data, such ashealthy body temperature, appear to be, about 95% of measurements arewithin 2 standard deviations from the average. Therefore, in anexemplary embodiment, the channel multiplier M1 is 2. Further, theperson's personal warning temperature is calculated using the average,the standard deviation, and another multiplier M2 such that the personalwarning temperature is M2 times the standard deviation above the averagetemperature.

In mathematical terms representing the exemplary embodiment, if theaverage body temperature is A, and the standard deviation is Sd, thenthe channel of expected normal temperatures for the healthy person isdefined by the lower bound, L, and the upper bound, U, and the personalwarning temperature, W, is defined as:L=A−(M1*Sd)U=A+(M1*Sd)W=A+(M2*Sd)

Many factors affect an individual's personal warning temperature such astime-of-day. An exemplary embodiment of PWT analyzes a healthy person'sbody temperature measurements in order to correct for time-of-daydifferences or interpret a body temperature measurement in order to taketime-of-day into account.

Measurement methods also affect the body temperature value. For example,different thermometers may measure the body temperature differently;and, in addition to time-of-day, the location on the body that thetemperature is taken also affects the value. For the determination offever, however, the particular measurement device utilized and method isnot as important as making sure the temperature is measured in asystematic and consistent way every time, so that the set ofmeasurements are precise, if not accurate. It is not so important whatthe particular temperature level is. Instead, it is only important tounderstand what the normal range of temperatures is when healthy; haverelatively small variation in the range of normal temperatures, and toknow when the temperature rises above the normal range of values,indicating possible illness.

In an exemplary embodiment of PWT, a healthy person's body temperaturemeasurements are plotted on a graph against the time of measurementalong with the channel of expected normal temperatures and the personalwarning temperature to facilitate understanding of body temperaturehistory; and observe very clearly when a particular, future bodytemperature, rises above the personal warning temperature.

FIG. 1 illustrates a graph (1) of body temperature, measured in degreesFahrenheit (4), with respect to time, measured in elapsed days (3), usedin an embodiment. Other exemplary embodiments use other temperature ortime units. There is no body temperature illustrated in FIG. 1. However,the traditional definition of the fever threshold, 100.4 degreesFahrenheit (2), is shown.

FIG. 2 illustrates the same graph (1) of FIG. 1 but now with some bodytemperature measurements (20) added for the temperature of anindividual. PWT requires at least two body temperature measurements forthe person, when healthy, to calculate non-trivial values for theaverage body temperature and standard deviation. In FIG. 2, there areten body temperature measurements (20) taken at different times. In anexemplary embodiment, PWT uses a few body temperature measurements tocalculate a non-changing channel and personal warning temperature. Inanother exemplary embodiment, the channel and personal warningtemperature are calculated from all normal body temperaturemeasurements, a moving time window of normal body temperaturemeasurements, or some set of normal and/or all body temperaturemeasurements.

FIG. 3 illustrates the graph (1) of FIG. 1 with the average bodytemperature (32), the lower (31) and upper (33) bounds defining theexpected normal temperature channel for the person when healthy, and theperson's personal warning temperature (34).

FIG. 4 illustrates the graph (1) of FIG. 1 with at least 20 days of theperson's body temperature data, now distinguished by whether thetemperature measurement was obtained in the morning (40) or evening (4).Because of circadian rhythm, body temperature is often higher in theevening than it is in the morning, assuming the person sleeps at nightas most people do (if not, then a different but explainable pattern canbe observed). In an exemplary embodiment, the person measures bodytemperature at the same time each day to remove the time-of-day effects.In another exemplary embodiment, the person uses a morning hour eachday. In yet another exemplary embodiment, the person uses an eveninghour each day. In yet a further exemplary embodiment, the person usesboth a morning hour and an evening hour.

FIG. 5 shows only body temperature measurements (40) of the person forthe morning hour. In an embodiment, the average (52) considering onlymorning-hour body temperature measurement (40) will be lower than theaverage considering all body temperature measurements (40 and 41) inFIG. 4. Similarly, the personal warning temperature (54), calculatedusing only morning-hour body temperature measurement (40) will be lowerthan the personal warning temperature (34) considering all bodytemperature measurements (40 and 41) in FIG. 4 and also shown in FIG. 5(34), the difference being shown (55).

FIG. 6 shows only body temperature measurements (41) of the person forthe evening hour. In an embodiment, the average (62) considering onlyevening-hour body temperature measurements will be higher than theaverage considering all body temperature measurements (40 and 41) inFIG. 4. Similarly, the personal warning temperature (64), calculatedusing only evening-hour body temperature measurement (41) will be higherthan the personal warning temperature (34) considering all bodytemperature measurements (40 and 41) in FIG. 4 and also shown in FIG. 6(34), the difference being shown (65).

FIG. 7 shows the result of analyzing the person's body temperaturemeasurements and correcting the channel and personal warning temperaturefor the effect of time-of-day variations expected for a person's bodytemperature. It is commonly known that a person's body temperaturevaries throughout the day in a phenomenon known as circadian rhythm.There are two ways to address time-of-day expected variations in bodytemperature. In an embodiment, indicated by FIG. 7, the personal warningtemperature and channel are modified to enable correct interpretation ofthe body temperature measured. In another exemplary embodiment, themeasured body temperature is adjusted for time-of-day expectedvariations and plotted on a graph with fixed values defining the channeland personal warning temperature.

There are several well-known methods for modeling the time-of-dayexpected variations in body temperature. For example, normal bodytemperature measurements can be used to find a mathematical functionthat fits the curve to the data when plotted on a graph. Alternatively,machine learning methods may be used to define a function.

There may be other identifiable factors that affect body temperaturemeasurement in predictable ways. In an exemplary embodiment of PWT, ahealthy person's body temperature measurements are analyzed, usingmachine learning or an equivalent technique, to determine how otherfactors affect body temperature and calculates a correction or method ofinterpreting body temperature in the context of those other factors.

Although different exemplary embodiments have been shown and described,other exemplary embodiments would be readily understood by an artisan.The claims are not to be limited by the embodiments disclosed but ratherby the scope of the appended claims.

The invention claimed is:
 1. A method for indicating an internaltemperature of an individual relative to a determined warningtemperature curve of the individual, the method comprising: recording,over at least one 24-hour period, a series of pairs of temperature andtime data, each data pair including a measured baseline internaltemperature of the individual using a temperature sensor and acorresponding temperature measurement time of day; determining amathematical function that fits an average temperature curve over theseries of data pairs for the at least one 24-hour period, wherein themathematical function is determined by machine learning methods;calculating a standard deviation channel width based on the time of dayfor the series of baseline internal temperatures of the individual, thestandard deviation channel width defining an upper temperature curvewith respect to the average temperature curve over the series of datapairs; determining, based on a product of the standard deviation channelwidth and a standard deviation multiplier, a warning temperature curveof the individual based on the time of day, the warning temperaturecurve being greater than the upper temperature curve; obtaining a secondinternal temperature of the individual using the temperature sensor; andproviding an indication of the second internal temperature of theindividual relative to the warning temperature curve.
 2. The method ofclaim 1, wherein obtaining the series of pairs of temperature and timedata of the individual are further obtained by at least one of: a sametemperature sensor for each of the series of baseline internaltemperatures; a similar location on the body of the individual for eachof the series of baseline internal temperatures.
 3. The method of claim1, wherein providing an indication of the second internal temperature ofthe individual relative to the warning temperature curve furtherincludes at least one of: displaying the warning temperature curve on agraph; and displaying the series of baseline internal temperatures onthe graph.
 4. The method of claim 1, wherein each of the series ofbaseline internal temperatures are separated by a similar period of timeover the at least one 24-hour period.
 5. The method of claim 4, whereinthe at least one 24-hour period includes a plurality of 24-hour periodsof time.
 6. The method of claim 1, further including adjusting ameasured baseline internal temperature of the individual based on timeof day expected variations.
 7. The method of claim 6, wherein the timeof day expected variations are visually represented relative to fixedvalues of the standard deviation channel width and the warningtemperature curve.
 8. The method of claim 1, further comprising:obtaining a second internal temperature of the individual using thetemperature sensor; determining a second standard deviation channelwidth based on the time of day including the second internal temperatureand the series of baseline internal temperatures of the individual tofurther determine, based on a second product of the standard deviationchannel width and a first standard deviation multiplier, a secondwarning temperature curve of the individual based on the time of day,the second warning temperature curve being greater than the uppertemperature curve; providing an indication of the second warningtemperature curve to the individual.
 9. The method of claim 1, whereinthe standard deviation channel width includes at least 95-percent of theobtained series of baseline internal temperatures of the individual. 10.The method of claim 1, wherein the determined warning temperature curveis less than 100.4 degrees Fahrenheit.
 11. A method of providing awarning temperature curve of an individual, the method comprising:recording, over at least one 24-hour period, a series of pairs oftemperature and time data, each data pair including a measured baselineinternal temperature of the individual using a temperature sensor and acorresponding temperature measurement time of day; determining amathematical function that fits an average temperature curve over theseries of data pairs for the at least one 24-hour period, wherein themathematical function is determined by machine learning methods;calculating a standard deviation channel width based on the time of dayfor the series of baseline internal temperatures of the individual, thestandard deviation channel width defining an upper temperature curvewith respect to the average temperature curve over the series of datapairs; determining, based on a product of the standard deviation channelwidth and a standard deviation multiplier, a warning temperature curveof the individual based on the time of day, the warning temperaturecurve being greater than the upper temperature curve; and providing anindication of the second internal temperature of the individual relativeto the warning temperature curve.
 12. The method of claim 11, whereinproviding the indication of the warning temperature curve of theindividual relative to the series of baseline internal temperaturesfurther includes at least one of: displaying the warning temperaturecurve on a graph; and displaying the series of baseline internaltemperatures on the graph.
 13. The method of claim 11, furthercomprising: obtaining a second internal temperature of the individualusing the temperature sensor; determining a second standard deviationchannel width based on the time of day including the second internaltemperature and the series of baseline internal temperatures of theindividual to further determine, based on a second product of thestandard deviation channel width and a first standard deviationmultiplier, a second warning temperature curve of the individual basedon the time of day, the second warning temperature curve being greaterthan the upper temperature curve; providing an indication of the secondwarning temperature curve to the individual.
 14. The method of claim 11,wherein the standard deviation channel width includes at least95-percent of the obtained series of baseline internal temperatures ofthe individual.
 15. The method of claim 11, wherein the determinedwarning temperature curve is less than 100.4 degrees Fahrenheit.
 16. Amethod of identifying a warning temperature curve of an individual, themethod comprising: recording, over at least one 24-hour period, a seriesof pairs of temperature and time data, each data pair including ameasured baseline internal temperature of the individual using atemperature sensor and a corresponding temperature measurement time ofday; determining a mathematical function that fits an averagetemperature curve over the series of data pairs for the at least one24-hour period, wherein the mathematical function is determined bymachine learning methods; calculating a standard deviation channel widthbased on the time of day for the series of baseline internaltemperatures of the individual, the standard deviation channel widthdefining an upper temperature curve with respect to the averagetemperature curve over the series of data pairs; determining, based on aproduct of the standard deviation channel width and a standard deviationmultiplier, a warning temperature curve of the individual based on thetime of day, the warning temperature curve being greater than the uppertemperature curve; and identifying, to the individual, the warningtemperature curve of the individual.
 17. The method of claim 16, whereinidentifying, to the individual, the warning temperature curve of theindividual, further includes at least one of: displaying the warningtemperature curve on a graph; and displaying the series of baselineinternal temperatures on the graph.
 18. The method of claim 16, furthercomprising: obtaining a second internal temperature of the individualusing the temperature sensor; determining a second standard deviationchannel width based on the time of day including the second internaltemperature and the series of baseline internal temperatures of theindividual to further determine, based on a second product of thestandard deviation channel width and a first standard deviationmultiplier, a second warning temperature curve of the individual basedon the time of day, the second warning temperature curve being greaterthan the upper temperature curve; providing an indication of the secondwarning temperature curve to the individual.
 19. The method of claim 16,wherein the standard deviation channel width includes at least95-percent of the obtained series of baseline internal temperatures ofthe individual, and wherein the determined warning temperature curve isless than 100.4 degrees Fahrenheit.
 20. A method for indicating aninternal temperature of an individual relative to a determined warningtemperature curve of the individual, the method comprising: recording,over at least one 24-hour period, a series of pairs of temperature andtime data, each data pair including a measured baseline internaltemperature of the individual using a temperature sensor and acorresponding temperature measurement time of day; determining amathematical function that fits an average temperature curve over theseries of data pairs for the at least one 24-hour period; calculating astandard deviation channel width based on the time of day for the seriesof baseline internal temperatures of the individual, the standarddeviation channel width defining an upper temperature curve with respectto the average temperature curve over the series of data pairs;determining, based on a product of the standard deviation channel widthand a standard deviation multiplier, a warning temperature curve of theindividual based on the time of day, the warning temperature curve beinggreater than the upper temperature curve; obtaining a second internaltemperature of the individual using the temperature sensor; providing anindication of the second internal temperature of the individual relativeto the warning temperature curve; and adjusting a measured baselineinternal temperature of the individual based on time of day expectedvariations, wherein the time of day expected variations are visuallyrepresented relative to fixed values of the standard deviation channelwidth and the warning temperature curve.